Manufacture of CD22 CAR T cells following positive versus negative selection results in distinct cytokine secretion profiles and γδ T cell output
Bibliographic record
Abstract
Chimeric antigen receptor T cells (CART) have demonstrated curative potential for hematological malignancies, but the optimal manufacturing has not yet been determined and may differ across products. The first step, T cell selection, removes contaminating cell types that can potentially suppress T cell expansion and transduction. While positive selection of CD4/CD8 T cells after leukapheresis is often used in clinical trials, it may modulate signaling cascades downstream of these co-receptors; indeed, the addition of a CD4/CD8-positive selection step altered CD22 CART potency and toxicity in patients. While negative selection may avoid this drawback, it is virtually absent from good manufacturing practices. Here, we performed both CD4/CD8-positive and -negative clinical scale selections of mononuclear cell apheresis products and generated CD22 CARTs per our ongoing clinical trial (NCT02315612NCT02315612). While the selection process did not yield differences in CART expansion or transduction, positively selected CART exhibited a significantly higher in vitro interferon-γ and IL-2 secretion but a lower in vitro tumor killing rate. Notably, though, CD22 CART generated from both selection protocols efficiently eradicated leukemia in NSG mice, with negatively selected cells exhibiting a significant enrichment in γδ CD22 CART. Thus, our study demonstrates the importance of the initial T cell selection process in clinical CART manufacturing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".